
import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase    :   Optional[int]						=			16
lowercase    :   Any						=			32




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Accelerator		,    _lowerCamelCase					:     int = 16		,    _lowerCamelCase					:     str = "bert-base-cased")   ->       List[str]:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   AutoTokenizer.from_pretrained(_lowerCamelCase)
							__UpperCamelCase      :  Optional[int]       =   load_dataset("glue"		,    "mrpc")

							def tokenize_function(_lowerCamelCase					:     int):
														# max_length=None => use the model max length (it's actually the default)
														__UpperCamelCase      :  Optional[Any]       =   tokenizer(examples["sentence1"]		,    examples["sentence2"]		,    truncation=_lowerCamelCase		,    max_length=_lowerCamelCase)
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							__UpperCamelCase      :  Optional[int]       =   datasets.map(
							    _lowerCamelCase		,    batched=_lowerCamelCase		,    remove_columns=["idx", "sentence1", "sentence2"]		,    load_from_cache_file=_lowerCamelCase)

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							__UpperCamelCase      :  Optional[Any]       =   tokenized_datasets.rename_column("label"		,    "labels")

							def collate_fn(_lowerCamelCase					:     Dict):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(_lowerCamelCase		,    padding="max_length"		,    max_length=128		,    return_tensors="pt")
														return tokenizer.pad(_lowerCamelCase		,    padding="longest"		,    return_tensors="pt")

							# Instantiate dataloaders.
							__UpperCamelCase      :  List[str]       =   DataLoader(
							    tokenized_datasets["train"]		,    shuffle=_lowerCamelCase		,    collate_fn=_lowerCamelCase		,    batch_size=_lowerCamelCase)
							__UpperCamelCase      :  Tuple       =   DataLoader(
							    tokenized_datasets["validation"]		,    shuffle=_lowerCamelCase		,    collate_fn=_lowerCamelCase		,    batch_size=_lowerCamelCase)

							return train_dataloader, eval_dataloader




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Tuple		,    _lowerCamelCase					:     Any		,    _lowerCamelCase					:     Any		,    _lowerCamelCase					:     str)   ->       int:






							'''simple docstring'''





							model.eval()
							__UpperCamelCase      :  Optional[int]       =   0
							for step, batch in enumerate(_lowerCamelCase):
														# We could avoid this line since we set the accelerator with `device_placement=True`.
														batch.to(accelerator.device)
														with torch.no_grad():
																					__UpperCamelCase      :  Optional[Any]       =   model(**_lowerCamelCase)
														__UpperCamelCase      :  str       =   outputs.logits.argmax(dim=-1)
														# It is slightly faster to call this once, than multiple times
														__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   accelerator.gather(
														    (predictions, batch["labels"]))  # If we are in a multiprocess environment, the last batch has duplicates
														if accelerator.use_distributed:
																					if step == len(_lowerCamelCase) - 1:
																												__UpperCamelCase      :  Union[str, Any]       =   predictions[: len(eval_dataloader.dataset) - samples_seen]
																												__UpperCamelCase      :  int       =   references[: len(eval_dataloader.dataset) - samples_seen]
																					else:
																												samples_seen += references.shape[0]
														metric.add_batch(
														    predictions=_lowerCamelCase		,    references=_lowerCamelCase		,    )

							__UpperCamelCase      :  List[Any]       =   metric.compute()
							return eval_metric["accuracy"]




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     Optional[int])   ->       List[str]:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__UpperCamelCase      :  List[str]       =   config["lr"]
							__UpperCamelCase      :  Tuple       =   int(config["num_epochs"])
							__UpperCamelCase      :  Optional[int]       =   int(config["seed"])
							__UpperCamelCase      :  Union[str, Any]       =   int(config["batch_size"])
							__UpperCamelCase      :  Dict       =   args.model_name_or_path

							set_seed(_lowerCamelCase)
							__UpperCamelCase  ,		__UpperCamelCase      :  Dict       =   get_dataloaders(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__UpperCamelCase      :  str       =   AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase		,    return_dict=_lowerCamelCase)

							# Instantiate optimizer
							__UpperCamelCase      :  List[str]       =   (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							__UpperCamelCase      :  Tuple       =   optimizer_cls(params=model.parameters()		,    lr=_lowerCamelCase)

							if accelerator.state.deepspeed_plugin is not None:
														__UpperCamelCase      :  List[str]       =   accelerator.state.deepspeed_plugin.deepspeed_config[
														    "gradient_accumulation_steps"
														]
							else:
														__UpperCamelCase      :  Union[str, Any]       =   1
							__UpperCamelCase      :  Dict       =   (len(_lowerCamelCase) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														__UpperCamelCase      :  List[str]       =   get_linear_schedule_with_warmup(
														    optimizer=_lowerCamelCase		,    num_warmup_steps=0		,    num_training_steps=_lowerCamelCase		,    )
							else:
														__UpperCamelCase      :  Any       =   DummyScheduler(_lowerCamelCase		,    total_num_steps=_lowerCamelCase		,    warmup_num_steps=0)

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Optional[Any]       =   accelerator.prepare(
							    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# We need to keep track of how many total steps we have iterated over
							__UpperCamelCase      :  Any       =   0
							# We also need to keep track of the stating epoch so files are named properly
							__UpperCamelCase      :  List[Any]       =   0
							__UpperCamelCase      :  List[str]       =   evaluate.load("glue"		,    "mrpc")
							__UpperCamelCase      :  Dict       =   num_epochs

							if args.partial_train_epoch is not None:
														__UpperCamelCase      :  Optional[int]       =   args.partial_train_epoch

							if args.resume_from_checkpoint:
														accelerator.load_state(args.resume_from_checkpoint)
														__UpperCamelCase      :  Any       =   args.resume_from_checkpoint.split("epoch_")[1]
														__UpperCamelCase      :  Any       =   ""
														for char in epoch_string:
																					if char.isdigit():
																												state_epoch_num += char
																					else:
																												break
														__UpperCamelCase      :  Tuple       =   int(_lowerCamelCase) + 1
														__UpperCamelCase      :  Optional[int]       =   evaluation_loop(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)
														accelerator.print("resumed checkpoint performance:"		,    _lowerCamelCase)
														accelerator.print("resumed checkpoint's scheduler's lr:"		,    lr_scheduler.get_lr()[0])
														accelerator.print("resumed optimizers's lr:"		,    optimizer.param_groups[0]["lr"])
														with open(os.path.join(args.output_dir		,    F'state_{starting_epoch-1}.json')		,    "r") as f:
																					__UpperCamelCase      :  List[Any]       =   json.load(_lowerCamelCase)
																					assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																					assert (
																					    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																					), "Scheduler learning rate mismatch, loading from checkpoint failed"
																					assert (
																					    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																					), "Optimizer learning rate mismatch, loading from checkpoint failed"
																					assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																					return

    # Now we train the model
							__UpperCamelCase      :  Dict       =   {}
							for epoch in range(_lowerCamelCase		,    _lowerCamelCase):
														model.train()
														for step, batch in enumerate(_lowerCamelCase):
																					__UpperCamelCase      :  Tuple       =   model(**_lowerCamelCase)
																					__UpperCamelCase      :  Union[str, Any]       =   outputs.loss
																					__UpperCamelCase      :  Dict       =   loss / gradient_accumulation_steps
																					accelerator.backward(_lowerCamelCase)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1
														__UpperCamelCase      :  List[str]       =   F'epoch_{epoch}'
														__UpperCamelCase      :  List[str]       =   os.path.join(args.output_dir		,    _lowerCamelCase)
														accelerator.save_state(_lowerCamelCase)
														__UpperCamelCase      :  Tuple       =   evaluation_loop(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)
														__UpperCamelCase      :  Dict       =   accuracy
														__UpperCamelCase      :  Any       =   lr_scheduler.get_lr()[0]
														__UpperCamelCase      :  Optional[Any]       =   optimizer.param_groups[0]["lr"]
														__UpperCamelCase      :  str       =   epoch
														__UpperCamelCase      :  Union[str, Any]       =   overall_step
														accelerator.print(F'epoch {epoch}:'		,    _lowerCamelCase)

														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					with open(os.path.join(args.output_dir		,    F'state_{epoch}.json')		,    "w") as f:
																												json.dump(_lowerCamelCase		,    _lowerCamelCase)




def       _SCREAMING_SNAKE_CASE   (						)   ->       Tuple:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage.")
							parser.add_argument(
							    "--model_name_or_path"		,    type=_lowerCamelCase		,    default="bert-base-cased"		,    help="Path to pretrained model or model identifier from huggingface.co/models."		,    required=_lowerCamelCase		,    )
							parser.add_argument(
							    "--output_dir"		,    type=_lowerCamelCase		,    default="."		,    help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,    )
							parser.add_argument(
							    "--resume_from_checkpoint"		,    type=_lowerCamelCase		,    default=_lowerCamelCase		,    help="If the training should continue from a checkpoint folder."		,    )
							parser.add_argument(
							    "--partial_train_epoch"		,    type=_lowerCamelCase		,    default=_lowerCamelCase		,    help="If passed, the training will stop after this number of epochs."		,    )
							parser.add_argument(
							    "--num_epochs"		,    type=_lowerCamelCase		,    default=2		,    help="Number of train epochs."		,    )
							__UpperCamelCase      :  Optional[Any]       =   parser.parse_args()
							__UpperCamelCase      :  Dict       =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

							training_function(_lowerCamelCase		,    _lowerCamelCase)


if __name__ == "__main__":
		main()

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase    :   List[str]						=			{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			[
		    'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'GPTBigCodeForSequenceClassification',
		    'GPTBigCodeForTokenClassification',
		    'GPTBigCodeForCausalLM',
		    'GPTBigCodeModel',
		    'GPTBigCodePreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_bigcode import (
				    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTBigCodeForCausalLM,
				    GPTBigCodeForSequenceClassification,
				    GPTBigCodeForTokenClassification,
				    GPTBigCodeModel,
				    GPTBigCodePreTrainedModel,
				)


else:
		import sys

		lowercase    :   Tuple						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)